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Last Posted: Apr 17, 2024
- Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.
Francisco Lopez-Jimenez et al. JACC Clin Electrophysiol 2024 - Deep learning evaluation of echocardiograms to identify occult atrial fibrillation.
Neal Yuan et al. NPJ Digit Med 2024 7(1) 96 - Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms.
Odeh Adeyi Victor et al. Sensors (Basel) 2024 24(7) - A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms.
Utkarsh Gupta et al. Heliyon 2024 10(5) e26787 - Utilizing ultra-early continuous physiologic data to develop automated measures of clinical severity in a traumatic brain injury population.
Shiming Yang et al. Sci Rep 2024 14(1) 7618 - Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms.
Ryuichiro Yagi et al. Nat Commun 2024 15(1) 2536 - An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.
Liong-Rung Liu et al. Heliyon 2024 10(5) e27200 - Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.
River Jiang et al. JAMA Cardiol 2024 - Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.
Jingjing Fan et al. Stress Health 2024 e3386 - An ECG-based artificial intelligence model for assessment of sudden cardiac death risk
L Holmstrom et al, Comm Med, February 2024 - "Inherited cardiovascular disease mindset" can identify concealed inherited conditions at cardio-oncology evaluation: An opportunistic screening.
Rebeca Lorca et al. Int J Cardiol 2024 131825 - Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.
Khaled Abdelrahman et al. Diagnostics (Basel) 2024 14(2) - Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.
Veer Sangha et al. medRxiv 2024 - Concealed Inherited Cardiomyopathies Detected in Cardio-Oncology Screening.
Rebeca Lorca et al. J Clin Med 2024 13(1) - A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction.
Liam Butler et al. Cardiovasc Digit Health J 2024 4(6) 183-190 - Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG.
Samir Awasthi et al. EClinicalMedicine 2023 65102259 - Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction Using Electrocardiographic Artificial Intelligence: A Cost-Effective Approach.
Wei-Ting Liu et al. Can J Cardiol 2023 - Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias
M Gadaleta et al, NPJ Digital Medicine, December 12, 2023 - Single-lead ECG AI model with risk factors detects Atrial Fibrillation during Sinus Rhythm.
Stijn Dupulthys et al. Europace 2023 - A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism.
Yu-Cheng Chen et al. Acta Cardiol Sin 2023 39(6) 913-928
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About HLBS-PopOmics
HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Site Citation:
Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
- Page last updated:Apr 25, 2024
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